Ultralow‐Power Machine Vision with Self‐Powered Sensor Reservoir

Abstract A neuromorphic visual system integrating optoelectronic synapses to perform the in‐sensor computing is triggering a revolution due to the reduction of latency and energy consumption. Here it is demonstrated that the dwell time of photon‐generated carriers in the space‐charge region can be effectively extended by embedding a potential well on the shoulder of Schottky energy barrier. It permits the nonlinear interaction of photocurrents stimulated by spatiotemporal optical signals, which is necessary for in‐sensor reservoir computing (RC). The machine vision with the sensor reservoir constituted by designed self‐powered Au/P(VDF‐TrFE)/Cs2AgBiBr6/ITO devices is competent for both static and dynamic vision tasks. It shows an accuracy of 99.97% for face classification and 100% for dynamic vehicle flow recognition. The in‐sensor RC system takes advantage of near‐zero energy consumption in the reservoir, resulting in decades‐time lower training costs than a conventional neural network. This work paves the way for ultralow‐power machine vision using photonic devices.


The photocurrents affected by polarization orientation
In order to quantify how polarization of ferroelectric P(VDF-TrFE) layer affects Cs 2 AgBiBr 6 , the device was investigated by applying 10 optical pulses after upward polarization and downward polarization, and the pristine state was presented as comparison ( Figure S4a-c). The pristine state corresponds to the state without any polarization at the ferroelectric layer (Note that all photonic-synaptic behaviors mentioned in this work were stimulated at pristine state). Upward polarization and downward polarization state refer to the polarization direction of P(VDF-TrFE) pointing up or down from the ITO substrate (inset of Figure S4a-c). The ferroelectricity of P(VDF-TrFE) is confirmed by the P-V hysteresis loop of an Au/P(VDF-TrFE)/ITO capacitor ( Figure S5). The device was set into the upward/downward polarization state by applying electrical pulse with voltage of -10 V/+10 V before applying optical stimulation. After the P(VDF-TrFE) was polarized, the electrical pulse was removed, 10 optical pulses with power density of 36.6 μW/cm 2 and width of 100 ms were applied. The facilitation (A n /A 1 ) stimulated in these three states mentioned above were carefully compared at each optical pulse in Figure 1h. Obviously, in the upward polarization, the EPSC facilitation was larger than the pristine state under the same illumination, and smaller than the pristine state while in downward polarization state. The reason for this phenomenon could be explained by the polarization of P(VDF-TrFE) ferroelectric layer, which would affect the band bending between the interface of P(VDF-TrFE) and Cs 2 AgBiBr 6 .
The high binding energy of the valence electron states in P(VDF-TrFE) layer gives an energy potential well for hole carriers at the P(VDF-TrFE)/Cs 2 AgBiBr 6 interface. This energy potential well makes the photon-generated hole carriers difficult to migrated to the Au electrode, which extends the lifetime of EPSC after removing the optical stimulus. This is the reason of the observed long-tail photocurrents and their facilitation under multi optical stimuluses. In the upward polarization, energy potential well is deeper, which gives a photocurrent with smaller amplitude but longer tail under each optical pulse. Their longer tails enhance the facilitation effect (coupling strength) under multi optical stimuluses ( Figure S4e).
In the downward polarization, energy potential well is reduced, which gives a photocurrent with large amplitude but short tail under each optical pulse. Their short tails weaken the facilitation effect (coupling strength) under multi optical stimuluses ( Figure S4f).

Supplementary note 4
During the In-sensor reservoir computing for image classification, the amplitude of EPSC at 35 s are collected from 28 reservoir devices as the new feature space y(t). An activation function was used to further differentiate the intensity distribution in y(t) and convert y(t) to voltage inputs for the following network. The activation function is shown below: Here I j is the EPSC at 35 s, and V j (n) is the voltage that used as the input in the simulation for the nth image.
Following the flow chart in the right panel of Figure 4c, a training simulation for image classification are performed on the following 28×4 memristor network using the new y(t) inputs and Manhattan update rule. [4,5] V j (n) is the input voltage representing the column j of nth image, W ij is the conductance of each memristor in the simulation arrays, β is a parameter that controls the non-linearity, (n) is the target value of the ith image and η is a constant scaling the training rate. During the training process, each class was inputted three times repeatedly. The measured EPSC at t = 35 s of each class were processed via equation (1)